Optical networks are traditionally point-to-point systems in which all electrical to optical conversions occur at a single transmitter site and optical to electrical conversions occur at the same receiver site after a known fixed path. This means that all wavelengths start together, experience the same fiber lengths and types and then enter co-located receivers.
Whenever wavelengths travel through any given path, they will interact with other wavelengths travelling along the same path. There are interference effects generated that must be taken into account at the network design stage in order to ensure satisfactory performance.
In static network configurations, such as are found in traditional point-to-point systems, the interference effects may be calculated a priori using traditional mechanisms.
Optical wavelengths interact due to non-linear effects and noise degradation within optical fibers and optical amplifiers. Currently, in order to determine how degraded a wavelength or signal is when it enters a receiver, simulation tools are used to model the propagation of a wavelength through a path in the presence of other wavelengths.
Such simulations account for the major sources of non-linear degradation by simplifying and solving the non-linear Schrodinger equation:
                                                        ∂              A                                      ∂              z                                +                                    β              1                        ⁢                                          ∂                A                                            ∂                t                                              +                                    i              2                        ⁢                          β              2                        ⁢                                                            ∂                  2                                ⁢                A                                            ∂                                  t                  2                                                              +                                    α              2                        ⁢            A                          =                  i          ⁢                                          ⁢          γ          ⁢                                                  A                                      2                    ⁢          A                                    (        1        )            where A is the pulse amplitude, β1 is related to the group velocity of the dominant optical mode carrying the pulse, β2 is responsible for pulse broadening, γ is the non-linearity coefficient and z is the distance propagated within the fiber. This equation is used to determine the effects of group velocity dispersion and the intensity dependent refractive index only (dispersion and Self Phase Modulation (SPM)). Further levels of abstraction and additional simultaneous propagation equations are required to fully describe the processes which lead to Cross Phase Modulation (XPM) and Four Wave Mixing (FWM). Generally, simulation models are designed incorporating some simplifying assumptions about the parameters of equation (1) and employ other simplified relationships to approximate the effects of XPM and FWM.
The equations developed are then solved numerically for all wavelengths present and over the entire propagation length of fiber. This process is computationally intensive and requires significant processing time. In addition, a high level of user expertise is required to interpret the results.
Further, Cross-Phase Modulation (XPM) and Four Wave Mixing (FWM) effects must also be calculated rigorously from the results.
The current generation of optical communication networks includes Optical Add/Drop Multiplexing (OADM) and Photonic Cross-Connect (PXC) nodes in which wavelengths can branch onto alternate spans of fiber without undergoing optical to electrical conversion. That is a combination of wavelengths that start together do not necessarily have to end up in the same set of co-located receivers. Moreover, individual wavelengths of a set of wavelengths that arrives at a set of co-located receivers may each have traversed a separate path to get to the set of co-located receivers.
OADMs are typically statically configured at system setup to add or drop pre-selected wavelengths. By contrast, PXCs allow incoming wavelengths to be switched dynamically to a choice of output directions.
The introduction of PXCs herald the promise of agile optical networks, in which routing of wavelengths may be dynamically altered to accommodate fluctuations in the demand for services. Accordingly, carrier companies employing agile optical networking techniques may reduce the amount of stranded bandwidth in their existing optical network infrastructures.
Because carrier companies need to use their networks optimally in order to get the most value out of their capacity, such a dynamic ability to optimize network capacity may increase the revenue generated from a fixed amount of fiber capacity. For example, certain wavelengths can be devoted to commercial banking transactions during the weekday and assigned to carry internet traffic in the evening and on weekends. As the flexibility or agility in the optical layer increases, ultimately, carrier companies may be able to offer “capacity on demand”, in which wavelengths can be dynamically re-routed to provide capacity where it is needed most at a given time.
However, the introduction of agile optical networks requires a relatively quick assessment method to be made available to the network operator and/or the routing algorithm in order to determine if a possible optical signal path is viable for a desired type of traffic. In other words, prior to switching from a known communications path to an alternate configuration that may improve traffic flow in the network, an algorithm must be in place that will check to ensure that wavelengths moving to a new path will have adequate signal quality to be received when they reach their receiver. If the proposed path introduces unacceptable distortion, an alternative route should be considered.
Alternatively, if technology permits rapid dynamic switching of network paths, a sufficiently responsive viability proving mechanism is required. With such a mechanism, one could conceivably implement a proposed alternate configuration without determining its viability at the time of installation, and dynamically determine the viability of the new signal paths as required.
The traditional mechanisms for ensuring adequate signal performance are unsatisfactory because the non-linear simulations are too complex and too time-consuming to be applied in a dynamic switching environment.
In agile optical network applications, different wavelengths follow different paths through the network from their respective transmitter Tx to their respective receiver Rx. Different groupings of wavelengths will encounter different nodes and segments in their travels. We define a node as a network element such as a transmitter, receiver, static OADM, PXC, and dispersion compensator possibly co-located with one or more optional amplifiers. We define a segment as fiber spans and amplifier sites interconnecting two nodes. Different segments can have different arrangements of fibers, amplifiers, dispersion compensators etc.
Accordingly, if the simulation calculations were to be conducted, they would require a segment by segment analysis, taking into account each wavelength travelling along it. In an agile optical network, a routing change of a single wavelength may invalidate a large proportion of the simulation, which will have to be recalculated.
A number of attempts have been made to overcome the computational intensity required in demonstrating the viability of a proposed alternate routing.
For example, efforts have been made to reduce the complexity of the problem. For instance, special and expensive adjustable dispersion compensators may be introduced to account for different types of fiber.
Alternatively, a worst-case performance threshold may be established for each segment that cannot be exceeded. Typically, the reach of each wavelength path is significantly limited as a result of the imposition of these thresholds, with a consequent requirement of additional regenerative receive/transmit nodes to extend the overall network reach. The addition of such regenerative nodes drives network cost significantly higher.
Similarly, limits may be imposed on the signal power level introduced into each fiber span within a segment. Again, the reach of segment paths is limited as a result.
Another approach is to limit the type of customer traffic that will be accepted on the network. For example, one can reduce the traffic capacity by lowering the bit-rate, which will reduce the complexity of path viability analysis because non-linearities becomes less dominant. At lower bit-rates, systems are predominantly limited by noise effects. Noise analysis can be done very efficiently in comparison with a full distortion and noise simulation. However, this approach greatly reduces the system capacity due to the lower bit-rate, and is generally not a practical alternative. A concrete example is the difference between a 2.5 Gb/s DWDM system and 10 Gb/s DWDM system. The difference in capacity makes 10 Gb/s a much more attractive option, despite the increased complexity.
All of the foregoing are extremely limiting and often expensive approaches that belie the advantages of agile optical networks.
Others have attempted to perform an up-front viability analysis by taking every possible path and conducting extensive simulations to determine the limits of viability of the network during network design. Clearly, such approaches are only suitable for very simple and small networks, and are very easily subject to becoming obsolete. For example, referring to FIG. 1 it can be noted that there are 6 possible paths that need to be simulated. However, the wavelengths sent to each segment might vary as well. Therefore all six paths would need to be simulated with several possible wavelength variations in order to guarantee that the worst-case performance had been assessed and each path was viable. This figure shows only a small example which would only cover a small fraction of a carrier's nationwide network.